diff --git a/bob/bio/face/config/baseline/arcface_insightface.py b/bob/bio/face/config/baseline/arcface_insightface.py
index 06f071c592a841b6b2ca66ad1997dc86cda518be..3aae89491d77909db49a10d3c8ef010d66e37f4a 100644
--- a/bob/bio/face/config/baseline/arcface_insightface.py
+++ b/bob/bio/face/config/baseline/arcface_insightface.py
@@ -15,11 +15,15 @@ if "database" in locals():
else:
annotation_type = None
fixed_positions = None
+ memory_demanding = False
def load(annotation_type, fixed_positions=None):
transformer = embedding_transformer_112x112(
- ArcFaceInsightFace(memory_demanding=memory_demanding), annotation_type, fixed_positions, color_channel="rgb"
+ ArcFaceInsightFace(memory_demanding=memory_demanding),
+ annotation_type,
+ fixed_positions,
+ color_channel="rgb",
)
algorithm = Distance()
diff --git a/bob/bio/face/test/test_baselines.py b/bob/bio/face/test/test_baselines.py
index 2de487ade1ec3753ec208d87f9fbb6c8fa98a9e1..bbe3c4c9afeaa468fd789496e05f6ad88c81885a 100644
--- a/bob/bio/face/test/test_baselines.py
+++ b/bob/bio/face/test/test_baselines.py
@@ -59,7 +59,7 @@ def get_fake_samples_for_training():
]
-def run_baseline(baseline, samples_for_training=[]):
+def run_baseline(baseline, samples_for_training=[], target_scores=None):
biometric_references = get_fake_sample_set(purpose="bioref")
probes = get_fake_sample_set(purpose="probe")
@@ -78,6 +78,10 @@ def run_baseline(baseline, samples_for_training=[]):
checkpoint_scores = checkpoint_pipeline([], biometric_references, probes)
assert len(checkpoint_scores) == 1
assert len(checkpoint_scores[0]) == 1
+
+ if target_scores is not None:
+ np.allclose(target_scores, scores[0][0].data, atol=10e-3, rtol=10e-3)
+
assert np.isclose(scores[0][0].data, checkpoint_scores[0][0].data)
dirs = os.listdir(d)
@@ -109,41 +113,41 @@ def run_baseline(baseline, samples_for_training=[]):
@pytest.mark.slow
@is_library_available("tensorflow")
def test_facenet_baseline():
- run_baseline("facenet-sanderberg")
+ run_baseline("facenet-sanderberg", target_scores=[-0.9220775737526933])
@pytest.mark.slow
@is_library_available("tensorflow")
def test_inception_resnetv2_msceleb():
- run_baseline("inception-resnetv2-msceleb")
+ run_baseline("inception-resnetv2-msceleb", target_scores=[-0.43447269718504244])
@pytest.mark.slow
@is_library_available("tensorflow")
def test_inception_resnetv2_casiawebface():
- run_baseline("inception-resnetv2-casiawebface")
+ run_baseline("inception-resnetv2-casiawebface", target_scores=[-0.634583944368043])
@pytest.mark.slow
@is_library_available("tensorflow")
def test_inception_resnetv1_msceleb():
- run_baseline("inception-resnetv1-msceleb")
+ run_baseline("inception-resnetv1-msceleb", target_scores=[-0.44497649298306907])
@pytest.mark.slow
@is_library_available("tensorflow")
def test_inception_resnetv1_casiawebface():
- run_baseline("inception-resnetv1-casiawebface")
+ run_baseline("inception-resnetv1-casiawebface", target_scores=[-0.6411599976437636])
@pytest.mark.slow
@is_library_available("mxnet")
def test_arcface_insightface():
- run_baseline("arcface-insightface")
+ run_baseline("arcface-insightface", target_scores=[-0.0005965275677296544])
def test_gabor_graph():
- run_baseline("gabor_graph")
+ run_baseline("gabor_graph", target_scores=[0.4385451147418939])
# def test_lda():
diff --git a/bob/bio/face/test/test_embeddings.py b/bob/bio/face/test/test_embeddings.py
deleted file mode 100644
index 9432e3e06a283acb5ec7df54362040d8625db4b0..0000000000000000000000000000000000000000
--- a/bob/bio/face/test/test_embeddings.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import bob.bio.face
-import bob.io.base
-import numpy as np
-from bob.pipelines import Sample, wrap
-import pkg_resources
-from bob.bio.base.test.utils import is_library_available
-import pytest
-
-
-@pytest.mark.slow
-@is_library_available("tensorflow")
-def test_idiap_inceptionv2_msceleb():
- from bob.bio.face.embeddings.tf2_inception_resnet import (
- InceptionResnetv2_MsCeleb_CenterLoss_2018,
- )
-
- reference = bob.io.base.load(
- pkg_resources.resource_filename(
- "bob.bio.face.test", "data/inception_resnet_v2_msceleb_rgb.hdf5"
- )
- )
- np.random.seed(10)
- transformer = InceptionResnetv2_MsCeleb_CenterLoss_2018()
- data = (np.random.rand(3, 160, 160) * 255).astype("uint8")
- output = transformer.transform([data])[0]
- assert output.size == 128, output.shape
-
- # Sample Batch
- sample = Sample(data)
- transformer_sample = wrap(["sample"], transformer)
- output = [s.data for s in transformer_sample.transform([sample])][0]
-
- np.testing.assert_allclose(output, reference.flatten(), rtol=1e-5, atol=1e-4)
- assert output.size == 128, output.shape
-
-
-@pytest.mark.slow
-@is_library_available("tensorflow")
-def test_idiap_inceptionv2_msceleb_memory_demanding():
- from bob.bio.face.embeddings.tf2_inception_resnet import (
- InceptionResnetv2_MsCeleb_CenterLoss_2018,
- )
-
- reference = bob.io.base.load(
- pkg_resources.resource_filename(
- "bob.bio.face.test", "data/inception_resnet_v2_msceleb_rgb.hdf5"
- )
- )
- np.random.seed(10)
-
- transformer = InceptionResnetv2_MsCeleb_CenterLoss_2018(memory_demanding=True)
- data = (np.random.rand(3, 160, 160) * 255).astype("uint8")
- output = transformer.transform([data])[0]
- assert output.size == 128, output.shape
-
- # Sample Batch
- sample = Sample(data)
- transformer_sample = wrap(["sample"], transformer)
- output = [s.data for s in transformer_sample.transform([sample])][0]
-
- np.testing.assert_allclose(output[0], reference.flatten(), rtol=1e-5, atol=1e-4)
- assert output.size == 128, output.shape
-
-
-@pytest.mark.slow
-@is_library_available("tensorflow")
-def test_idiap_inceptionv2_casia():
- from bob.bio.face.embeddings.tf2_inception_resnet import (
- InceptionResnetv2_Casia_CenterLoss_2018,
- )
-
- reference = bob.io.base.load(
- pkg_resources.resource_filename(
- "bob.bio.face.test", "data/inception_resnet_v2_casia_rgb.hdf5"
- )
- )
- np.random.seed(10)
- transformer = InceptionResnetv2_Casia_CenterLoss_2018()
- data = (np.random.rand(3, 160, 160) * 255).astype("uint8")
- output = transformer.transform([data])[0]
- assert output.size == 128, output.shape
-
- # Sample Batch
- sample = Sample(data)
- transformer_sample = wrap(["sample"], transformer)
- output = [s.data for s in transformer_sample.transform([sample])][0]
-
- np.testing.assert_allclose(output, reference.flatten(), rtol=1e-5, atol=1e-4)
- assert output.size == 128, output.shape
-
-
-@pytest.mark.slow
-@is_library_available("tensorflow")
-def test_idiap_inceptionv1_msceleb():
- from bob.bio.face.embeddings.tf2_inception_resnet import (
- InceptionResnetv1_MsCeleb_CenterLoss_2018,
- )
-
- reference = bob.io.base.load(
- pkg_resources.resource_filename(
- "bob.bio.face.test", "data/inception_resnet_v1_msceleb_rgb.hdf5"
- )
- )
- np.random.seed(10)
- transformer = InceptionResnetv1_MsCeleb_CenterLoss_2018()
- data = (np.random.rand(3, 160, 160) * 255).astype("uint8")
- output = transformer.transform([data])[0]
- assert output.size == 128, output.shape
-
- # Sample Batch
- sample = Sample(data)
- transformer_sample = wrap(["sample"], transformer)
- output = [s.data for s in transformer_sample.transform([sample])][0]
-
- np.testing.assert_allclose(output, reference.flatten(), rtol=1e-5, atol=1e-4)
- assert output.size == 128, output.shape
-
-
-@pytest.mark.slow
-@is_library_available("tensorflow")
-def test_idiap_inceptionv1_casia():
- from bob.bio.face.embeddings.tf2_inception_resnet import (
- InceptionResnetv1_Casia_CenterLoss_2018,
- )
-
- reference = bob.io.base.load(
- pkg_resources.resource_filename(
- "bob.bio.face.test", "data/inception_resnet_v1_casia_rgb.hdf5"
- )
- )
- np.random.seed(10)
- transformer = InceptionResnetv1_Casia_CenterLoss_2018()
- data = (np.random.rand(3, 160, 160) * 255).astype("uint8")
- output = transformer.transform([data])[0]
- assert output.size == 128, output.shape
-
- # Sample Batch
- sample = Sample(data)
- transformer_sample = wrap(["sample"], transformer)
- output = [s.data for s in transformer_sample.transform([sample])][0]
-
- np.testing.assert_allclose(output, reference.flatten(), rtol=1e-5, atol=1e-4)
- assert output.size == 128, output.shape
-
-
-@pytest.mark.slow
-@is_library_available("tensorflow")
-def test_facenet_sanderberg():
- from bob.bio.face.embeddings.tf2_inception_resnet import (
- FaceNetSanderberg_20170512_110547,
- )
-
- reference = bob.io.base.load(
- pkg_resources.resource_filename(
- "bob.bio.face.test", "data/facenet_sandberg_20170512-110547.hdf5"
- )
- )
- np.random.seed(10)
- transformer = FaceNetSanderberg_20170512_110547()
- data = (np.random.rand(3, 160, 160) * 255).astype("uint8")
- output = transformer.transform([data])[0]
- assert output.size == 128, output.shape
-
- # Sample Batch
- sample = Sample(data)
- transformer_sample = wrap(["sample"], transformer)
- output = [s.data for s in transformer_sample.transform([sample])][0]
-
- np.testing.assert_allclose(output, reference.flatten(), rtol=1e-5, atol=1e-4)
- assert output.size == 128, output.shape
-
-
-@pytest.mark.slow
-@is_library_available("mxnet")
-def test_arcface_insight_face():
- from bob.bio.face.embeddings.mxnet_models import ArcFaceInsightFace
-
- transformer = ArcFaceInsightFace()
- data = np.random.rand(3, 112, 112) * 255
- data = data.astype("uint8")
- output = transformer.transform([data])
- assert output.size == 512, output.shape
-
- # Sample Batch
- sample = Sample(data)
- transformer_sample = wrap(["sample"], transformer)
- output = [s.data for s in transformer_sample.transform([sample])][0]
- assert output.size == 512, output.shape
diff --git a/bob/bio/face/test/test_transformers.py b/bob/bio/face/test/test_transformers.py
index 1af1a86d50c9cebdaf3cf46dca9356d1b64e4ce1..805a31485df14337a58f1f6d855fd32e66e00811 100644
--- a/bob/bio/face/test/test_transformers.py
+++ b/bob/bio/face/test/test_transformers.py
@@ -12,89 +12,6 @@ def get_fake_sample(face_size=(160, 160), eyes={"leye": (46, 107), "reye": (46,
return Sample(data, key="1", annotations=annotations)
-@pytest.mark.slow
-@is_library_available("tensorflow")
-def test_facenet_sanderberg():
- transformer = load_resource("facenet-sanderberg", "transformer")
-
- fake_sample = get_fake_sample()
-
- transformed_sample = transformer.transform([fake_sample])[0]
- transformed_data = transformed_sample.data
- assert transformed_sample.data.size == 128
-
-
-@pytest.mark.slow
-@is_library_available("tensorflow")
-def test_inception_resnetv2_msceleb():
- transformer = load_resource("inception-resnetv2-msceleb", "transformer")
-
- fake_sample = get_fake_sample()
-
- transformed_sample = transformer.transform([fake_sample])[0]
- transformed_data = transformed_sample.data
- assert transformed_sample.data.size == 128
-
-
-@pytest.mark.slow
-@is_library_available("tensorflow")
-def test_inception_resnetv2_casiawebface():
- transformer = load_resource("inception-resnetv2-casiawebface", "transformer")
-
- fake_sample = get_fake_sample()
-
- transformed_sample = transformer.transform([fake_sample])[0]
- transformed_data = transformed_sample.data
- assert transformed_sample.data.size == 128
-
-
-@pytest.mark.slow
-@is_library_available("tensorflow")
-def test_inception_resnetv1_msceleb():
- transformer = load_resource("inception-resnetv1-msceleb", "transformer")
-
- fake_sample = get_fake_sample()
-
- transformed_sample = transformer.transform([fake_sample])[0]
- transformed_data = transformed_sample.data
- assert transformed_sample.data.size == 128
-
-
-@pytest.mark.slow
-@is_library_available("tensorflow")
-def test_inception_resnetv1_casiawebface():
- transformer = load_resource("inception-resnetv1-casiawebface", "transformer")
-
- fake_sample = get_fake_sample()
-
- transformed_sample = transformer.transform([fake_sample])[0]
- transformed_data = transformed_sample.data
- assert transformed_sample.data.size == 128
-
-
-"""
-def test_arcface_insight_tf():
- import tensorflow as tf
- tf.compat.v1.reset_default_graph()
- transformer = load_resource("arcface-insight-tf", "transformer")
-
- fake_sample = get_fake_sample()
-
- transformed_sample = transformer.transform([fake_sample])[0]
- transformed_data = transformed_sample.data
- assert transformed_sample.data.size == 512
-"""
-
-
-def test_gabor_graph():
- transformer = load_resource("gabor-graph", "transformer")
-
- fake_sample = get_fake_sample()
- transformed_sample = transformer.transform([fake_sample])[0]
- transformed_data = transformed_sample.data
- assert len(transformed_sample.data) == 80
-
-
def test_lgbphs():
transformer = load_resource("lgbphs", "transformer")